[1]} is also an important work on how to apply the BERT architecture to the task of text classification. It researches how multiple hyperparameters for training BERT affect its performance on various Natural Language Understanding tasks. The authors propose 3 different text truncation strategies - head-only, tail-only, head-only, and head+tail. Their experiment shows that the head+tail method attains the best result. But we decide to use head-only instead of it since it is not that obvious how the head+tail method affects the topical shifts in data. Another hyperparameter we change is \(batch\_size\)  due to our limited computational resources. All the other recommendations from the paper are followed.
